Brian L Egleston, MPP, PhD
Member & Associate Research Professor
Office Phone: 215-214-3917
The appropriateness of comorbidity scores to account for clinical prognosis and confounding in observational studies, in collaboration with S.R. Austin, Y.N. Wong, R.G. Uzzo & J.R. Beck
Recently, we have been examining the conditions under which summary comorbidity scores are valid. Comorbidities are co-existing medical conditions that individuals might have in addition to an index condition of interest, such as cancer. Comorbidity adjustment is an important goal of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. While many health services researchers have compared the utility of comorbidity scores using data examples, there has been a lack of mathematical rigor in most of the evaluations. In the statistics literature, theoretical justifications have been given for the use of prognostic scores. Heuristically, comorbidity scores are diminutive versions of prognostic scores. They are often added to regressions with other covariates such as age and sex. We examined the conditions under which individual versus summary measures are most appropriate. We expand on other's work, and show that comorbidity scores created analogously to the Charlson Comorbidity Index may be appropriate balancing scores for prognostic modeling and comorbidity adjustment.